Optimization of water-alternating-gas (WAG) processes is critical for maximizing both oil recovery and carbon-sequestration efficiency in CO2 enhanced oil recovery (EOR) projects. Conventional optimization using simulation models can be cumbersome because of the vast design space and high uncertainty. In this study, a deep-learning model, the temporal fusion transformer (TFT), is applied for multivariate forecasting of oil production and CO2-sequestration efficiency across a range of WAG scenarios using field data from six legacy CO2 EOR projects.
Introduction
In this study, a novel, data-driven framework is presented that leverages TFT to forecast 12 months of CO2 and incremental oil production across six legacy CO2 EOR fields in the Permian Basin: East Vacuum (EV), Denver Unit (DU), Wasson San Andres (WSA), Seminole San Andres Unit (SSAU), Scurry Area Canyon Reef Operators (SACROC) Unit, and Rangely Weber Sand (RWS). These fields were selected because of their long production histories, public data availability, and diverse operational characteristics. Production and injection data were digitized from historical publications and technical papers and preprocessed to isolate the incremental oil response to CO2. For each field, the waterflood trend was extrapolated using an exponential decline curve to estimate baseline performance; the difference was attributed to CO2 injection response.
To evaluate short-term operational strategies, five different WAG scenarios were created per field by varying the gas/water ratio (GWR). These scenarios were input into the TFT model, which was trained on historical data and used to forecast monthly oil and CO2 production.
